Use of mathematical optimization to construct optimal reservoir operating rules—A case study of the Barna reservoir in Narmada Basin, India

Nesa Ilich

Article ID: 2256
Vol 6, Issue 2, 2023

VIEWS - 236 (Abstract) 105 (PDF)

Abstract


This paper explains the benefits of using mathematical optimization to construct high performance reservoir operating rules and the related water rationing (deficit sharing) policies. The principal idea of the proposed approach is to generate perfect solutions obtained from an LP-based optimization model with the assumed foreknowledge of inflows represented with historical natural flows that are matched in the model with the current or projected levels of water demands. Water demands may include a mix of on-stream (e.g., e-flow targets or hydro power) and off-stream demands (irrigation or industry). The paper demonstrates the benefits of the proposed methodology by developing and testing short term operating rules on the Barna reservoir in Narmada River Basin in India. It shows that it is possible to achieve simulated results that follow the proposed rules and differ by only 2.5% in terms of the mean annual deficits from the best possible performance obtained using mathematical optimization with full foreknowledge of inflows.


Keywords


mathematical optimization; reservoir operation; rule curves; water demand management

Full Text:

PDF


References


1. Wurbs RA. Reservoir-system simulation and optimization models. Journal of Water Resources Planning and Management 1993; 119(4): 455–471. doi: 10.1061/(ASCE)0733-9496(1993)119:4(455)

2. Labadie JW. Optimal operation of multireservoir systems: State-of-the-art review. Journal of Water Resources Planning and Management 2004; 130(2): 93–111. doi: 10.1061/(ASCE)0733-9496(2004)130:2(93)

3. Rani D, Moreira MM. Simulation-optimization modelling: A survey and potential application in reservoir systems operation. Water Resources Management 2010; 24: 1107–1138. doi: 10.1007/s11269-009-9488-0

4. Tomlinson JE, Arnott JH, Harou JJ. A water resource simulator in Python. Environmental Modelling and Software 2020; 126: 104635. doi: 10.1016/j.envsoft.2020.104635

5. Dobson B, Wagener T, Pianosi F. An argument-driven classification and comparison of reservoir operation optimization methods. Advances in Water Resources 2019; 128: 74–86. doi: 10.1016/j.advwatres.2019.04.012

6. Ilich N, Basistha A. Importance of multiple time step optimization in river basin planning and management: A case study of Damodar River basin in India. Hydrological Sciences Journal 2021; 66(5): 809–825. doi: 10.1080/02626667.2021.1895438

7. Koutsoyiannis D, Economou A. Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems. Water Resources Research 2003; 39(6): 1170–1187. doi: 10.1029/2003WR002148

8. Bhaskar NR, Whitlach EE Jr. Deriving of monthly reservoir release policies. Water Resources Research 1980; 16(6): 987–993. doi: 10.1029/WR016i006p00987

9. Revelle C, Joeres E, Kirby W. The linear decision rule in reservoir management and design: 1, development of the stochastic model. Water Resources Research 1969; 5(4): 767–777. doi: 10.1029/WR005i004p00767

10. Turgeon A. Stochastic optimization of multireservoir operation: The optimal reservoir trajectory approach. Water Resources Research 2007; 43(5). doi: 10.1029/2005WR004619

11. CALVIN project overview. Available online: https://calvin.ucdavis.edu/calvin-project-overview (accessed on 5 September 2023).

12. Ilich N, Davies EGR, Gharib A. New modelling paradigms for assessing future irrigation storage requirements: A case study of the Western irrigation district in Alberta. Canadian Water Resources Journal 2020; 45(2): 172–185. doi: 10.1080/07011784.2020.1737237

13. Zagona EA, Fulp TJ, Shane R, et al. RiverWare: A generalized tool for complex reservoir system modeling. Journal of the American Water Resources Association 2007; 37(4): 913–929. doi: 10.1111/j.1752-1688.2001.tb05522.x

14. Randall D, Cleland L, Kuehne CS, et al. Water supply planning simulation model using mixed-integer linear programming “engine”. Journal of Water Resources Planning and Management 1997; 123(2): 116–124. doi: 10.1061/(ASCE)0733-9496(1997)123:2(116)

15. Ilich N. WEB.BM—A web based river basin management model with multiple time step optimization and the SSARR channel routing options. Hydrological Sciences Journal 2022; 6(2): 175–190. doi: 10.1080/02626667.2021.2018134

16. Ilich N. Improving real time reservoir operation based on combining demand hedging and simple storage management rules. Journal of Hydroinformatics 2011; 13(3): 533–544. doi: 0.2166/hydro.2010.183

17. Gavahi K, Mousavi SJ, Ponnambalam K. Adaptive forecast-based real-time optimal reservoir operations: Application to Lake Urmia. Journal of Hydroinformatics 2019; 21(5): 908–924. doi: 10.2166/hydro.2019.005

18. Bellman R. Dynamic programming. Science 1966; 153(3731): 34–37. doi: 10.1126/science.153.3731.34

19. Labadie JW, Fontane DG, Lee JH, Ko IH. Decision support system for adaptive river basin management: Application to the Geum river basin, Korea. Water International 2007; 32(3): 397–415. doi: 10.1080/02508060708692220

20. Yates D, Sieber J, Purkey D, Huber-Lee A. WEAP21—A demand-, priority-, and preference-driven water planning model. Part 1: Model characteristics. Water International 2005; 30(4): 487–500. doi: 10.1080/02508060508691893

21. State of Victoria. Resource allocation model (REALM). Available online: https://www.water.vic.gov.au/water-reporting/surface-water-modelling/resource-allocation-model-realm (accessed on 31 August 2023).




DOI: https://doi.org/10.24294/nrcr.v6i2.2256

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Nesa Ilich

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This site is licensed under a Creative Commons Attribution 4.0 International License.